Vibration monitoring and health status recognition technology of machine tool electric spindle
Abstract This paper proposes a vibration monitoring and health status recognition model for machine tool electric spindles to optimize efficiency. Laser Doppler technology is used to obtain vibration signals, which are analyzed through wavelet transform. A health status detection vector is calculate...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | Journal of Engineering and Applied Science |
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| Online Access: | https://doi.org/10.1186/s44147-025-00672-2 |
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| _version_ | 1849399916031377408 |
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| author | Xiaopei Tao Yanping Zhao Yanwei Chen |
| author_facet | Xiaopei Tao Yanping Zhao Yanwei Chen |
| author_sort | Xiaopei Tao |
| collection | DOAJ |
| description | Abstract This paper proposes a vibration monitoring and health status recognition model for machine tool electric spindles to optimize efficiency. Laser Doppler technology is used to obtain vibration signals, which are analyzed through wavelet transform. A health status detection vector is calculated and compared with a standard vector in a database using Euclidean distance. The results showed that when spindle speed was below 5000 rpm, the vibration intensity growth rate was faster, while it slowed down above 5000 rpm. At 5000 rpm, the predicted and measured values of vibration intensity matched at 0.065. At 12,500 rpm, the values were 0.073 and 0.076, respectively. The test results indicated that the spindle was unbalanced when the test sequence number was between 24 and 40, and the bearing was faulty when the test sequence number was between 2 and 16. This method efficiently identifies fault data types and offers technical insights for vibration monitoring and health status recognition of machine tool electric spindles. |
| format | Article |
| id | doaj-art-a426f690ea97468aac84948a4e7affd0 |
| institution | Kabale University |
| issn | 1110-1903 2536-9512 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Engineering and Applied Science |
| spelling | doaj-art-a426f690ea97468aac84948a4e7affd02025-08-20T03:38:13ZengSpringerOpenJournal of Engineering and Applied Science1110-19032536-95122025-07-0172111810.1186/s44147-025-00672-2Vibration monitoring and health status recognition technology of machine tool electric spindleXiaopei Tao0Yanping Zhao1Yanwei Chen2Department of Mechanical and Electrical Engineering, Luohe Vocational Technology CollegeDepartment of Mechanical and Electrical Engineering, Luohe Vocational Technology CollegeDepartment of Mechanical and Electrical Engineering, Luohe Vocational Technology CollegeAbstract This paper proposes a vibration monitoring and health status recognition model for machine tool electric spindles to optimize efficiency. Laser Doppler technology is used to obtain vibration signals, which are analyzed through wavelet transform. A health status detection vector is calculated and compared with a standard vector in a database using Euclidean distance. The results showed that when spindle speed was below 5000 rpm, the vibration intensity growth rate was faster, while it slowed down above 5000 rpm. At 5000 rpm, the predicted and measured values of vibration intensity matched at 0.065. At 12,500 rpm, the values were 0.073 and 0.076, respectively. The test results indicated that the spindle was unbalanced when the test sequence number was between 24 and 40, and the bearing was faulty when the test sequence number was between 2 and 16. This method efficiently identifies fault data types and offers technical insights for vibration monitoring and health status recognition of machine tool electric spindles.https://doi.org/10.1186/s44147-025-00672-2Machine tool electric spindleVibration monitoringLaser Doppler vibrometerHealth status recognitionWavelet packet analysis technology |
| spellingShingle | Xiaopei Tao Yanping Zhao Yanwei Chen Vibration monitoring and health status recognition technology of machine tool electric spindle Journal of Engineering and Applied Science Machine tool electric spindle Vibration monitoring Laser Doppler vibrometer Health status recognition Wavelet packet analysis technology |
| title | Vibration monitoring and health status recognition technology of machine tool electric spindle |
| title_full | Vibration monitoring and health status recognition technology of machine tool electric spindle |
| title_fullStr | Vibration monitoring and health status recognition technology of machine tool electric spindle |
| title_full_unstemmed | Vibration monitoring and health status recognition technology of machine tool electric spindle |
| title_short | Vibration monitoring and health status recognition technology of machine tool electric spindle |
| title_sort | vibration monitoring and health status recognition technology of machine tool electric spindle |
| topic | Machine tool electric spindle Vibration monitoring Laser Doppler vibrometer Health status recognition Wavelet packet analysis technology |
| url | https://doi.org/10.1186/s44147-025-00672-2 |
| work_keys_str_mv | AT xiaopeitao vibrationmonitoringandhealthstatusrecognitiontechnologyofmachinetoolelectricspindle AT yanpingzhao vibrationmonitoringandhealthstatusrecognitiontechnologyofmachinetoolelectricspindle AT yanweichen vibrationmonitoringandhealthstatusrecognitiontechnologyofmachinetoolelectricspindle |